1932

Abstract

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-032818-121715
2019-03-25
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/food/10/1/annurev-food-032818-121715.html?itemId=/content/journals/10.1146/annurev-food-032818-121715&mimeType=html&fmt=ahah

Literature Cited

  1. Amato MC, Pizzolanti G, Torregrossa V, Panto F, Giordano C 2016. Phenotyping of type 2 diabetes mellitus at onset on the basis of fasting incretin tone: results of a two-step cluster analysis. J. Diabetes Investig. 7:219–25
    [Google Scholar]
  2. Andersen MB, Kristensen M, Manach C, Pujos-Guillot E, Poulsen SK et al. 2014. Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics. Anal. Bioanal. Chem. 406:1829–44
    [Google Scholar]
  3. Andersen M-BS, Reinbach HC, Rinnan A, Barri T, Mithril C, Dragsted LO 2013. Discovery of exposure markers in urine for Brassica-containing meals served with different protein sources by UPLC-qTOF-MS untargeted metabolomics. Metabolomics 9:984–97
    [Google Scholar]
  4. Arguelles W, Llabre MM, Sacco RL, Penedo FJ, Carnethon M et al. 2015. Characterization of metabolic syndrome among diverse Hispanics/Latinos living in the United States: latent class analysis from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Int. J. Cardiol. 184:373–79
    [Google Scholar]
  5. Badimon L, Vilahur G, Padro T 2017. Systems biology approaches to understand the effects of nutrition and promote health. Br. J. Clin. Pharmacol. 83:38–45
    [Google Scholar]
  6. Baenas N, Suárez-Martínez C, García-Viguera C, Moreno DA 2017. Bioavailability and new biomarkers of cruciferous sprouts consumption. Food Res. Int. 100:497–503
    [Google Scholar]
  7. Baldassarre ME, Di Mauro A, Tafuri S, Rizzo V, Gallone MS et al. 2018. Effectiveness and safety of a probiotic-mixture for the treatment of infantile colic: a double-blind, randomized, placebo-controlled clinical trial with fecal real-time PCR and NMR-based metabolomics analysis. Nutrients 10:E195
    [Google Scholar]
  8. Balderas C, Villaseñor A, García A, Rupérez FJ, Ibañez E et al. 2010. Metabolomic approach to the nutraceutical effect of rosemary extract plus ω-3 PUFAs in diabetic children with capillary electrophoresis. J. Pharm. Biomed. Anal. 53:1298–304
    [Google Scholar]
  9. Barton S, Navarro SL, Buas MF, Schwarz Y, Gu H et al. 2015. Targeted plasma metabolome response to variations in dietary glycemic load in a randomized, controlled, crossover feeding trial in healthy adults. Food Funct 6:2949–56
    [Google Scholar]
  10. Barupal DK, Fan S, Fiehn O 2018. Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets. Curr. Opin. Biotechnol. 54:1–9
    [Google Scholar]
  11. Barupal DK, Fiehn O 2017. Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci. Rep. 7:14567
    [Google Scholar]
  12. Bazanella M, Maier TV, Clavel T, Lagkouvardos I, Lucio M et al. 2017. Randomized controlled trial on the impact of early-life intervention with bifidobacteria on the healthy infant fecal microbiota and metabolome. Am. J. Clin. Nutr. 106:1274–86
    [Google Scholar]
  13. Beckmann M, Lloyd AJ, Haldar S, Seal C, Brandt K, Draper J 2013. Hydroxylated phenylacetamides derived from bioactive benzoxazinoids are bioavailable in humans after habitual consumption of whole grain sourdough rye bread. Mol. Nutr. Food Res. 57:1859–73
    [Google Scholar]
  14. Bertram HC, Hoppe C, Petersen BO, Duus JO, Molgaard C, Michaelsen KF 2007. An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br. J. Nutr. 97:758–63
    [Google Scholar]
  15. Bondia-Pons I, Poho P, Bozzetto L, Vetrani C, Patti L et al. 2014. Isoenergetic diets differing in their n-3 fatty acid and polyphenol content reflect different plasma and HDL-fraction lipidomic profiles in subjects at high cardiovascular risk. Mol. Nutr. Food Res. 58:1873–82
    [Google Scholar]
  16. Brennan L 2014. NMR-based metabolomics: from sample preparation to applications in nutrition research. Prog. Nucl. Magn. Reson. Spectrosc. 83:42–49
    [Google Scholar]
  17. Brouwer-Brolsma EM, Brennan L, Drevon CA, van Kranen H, Manach C et al. 2017. Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance. Proc. Nutr. Soc. 76:619–27
    [Google Scholar]
  18. Bruce SJ, Breton I, Decombaz J, Boesch C, Scheurer E et al. 2010. A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 878:3015–23
    [Google Scholar]
  19. Cajka T, Fiehn O 2016. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88:524–45
    [Google Scholar]
  20. Cesare Marincola F, Corbu S, Lussu M, Noto A, Dessi A et al. 2016. Impact of early postnatal nutrition on the NMR urinary metabolic profile of infant. J. Proteome Res. 15:3712–23
    [Google Scholar]
  21. Cheung W, Keski-Rahkonen P, Assi N, Ferrari P, Freisling H et al. 2017. A metabolomic study of biomarkers of meat and fish intake. Am. J. Clin. Nutr. 105:600–8
    [Google Scholar]
  22. Chong J, Soufan O, Li C, Caraus I, Li S et al. 2018. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46:W486–94
    [Google Scholar]
  23. Chorell E, Moritz T, Branth S, Antti H, Svensson MB 2009. Predictive metabolomics evaluation of nutrition-modulated metabolic stress responses in human blood serum during the early recovery phase of strenuous physical exercise. J. Proteome Res. 8:2966–77
    [Google Scholar]
  24. Chorell E, Ryberg M, Larsson C, Sandberg S, Mellberg C et al. 2016. Plasma metabolomic response to postmenopausal weight loss induced by different diets. Metabolomics 12:14
    [Google Scholar]
  25. Corella D, Coltell O, Macian F, Ordovas JM 2018. Advances in understanding the molecular basis of the Mediterranean diet effect. Annu. Rev. Food Sci. Technol. 9:227–49
    [Google Scholar]
  26. Cottret L, Wildridge D, Vinson F, Barrett MP, Charles H et al. 2010. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res 38:W132–37
    [Google Scholar]
  27. Covington BC, McLean JA, Bachmann BO 2017. Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites. Nat. Prod. Rep. 34:6–24
    [Google Scholar]
  28. Cross AJ, Major JM, Sinha R 2011. Urinary biomarkers of meat consumption. Cancer Epidemiol. Biomark. Prev. 20:1107–11
    [Google Scholar]
  29. Cuparencu CS, Andersen MBS, Gurdeniz G, Schou SS, Mortensen MW et al. 2016. Identification of urinary biomarkers after consumption of sea buckthorn and strawberry, by untargeted LC-MS metabolomics: A meal study in adult men. Metabolomics 12:31
    [Google Scholar]
  30. Daykin CA, Van Duynhoven JP, Groenewegen A, Dachtler M, Van Amelsvoort JM, Mulder TP 2005. Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J. Agric. Food Chem. 53:1428–34
    [Google Scholar]
  31. de Magalhães Cunha C, Costa PRF, De Oliveira LPM, Queiroz VAO, Pitangueira JCD, Oliveira AM 2018. Dietary patterns and cardiometabolic risk factors among adolescents: systematic review and meta-analysis. Br. J. Nutr. 119:859–79
    [Google Scholar]
  32. Dessi A, Murgia A, Agostino R, Pattumelli MG, Schirru A et al. 2016. Exploring the role of different neonatal nutrition regimens during the first week of life by urinary GC-MS metabolomics. Int. J. Mol. Sci. 17:265
    [Google Scholar]
  33. Dewulf EM, Cani PD, Claus SP, Fuentes S, Puylaert PG et al. 2013. Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut 62:1112–21
    [Google Scholar]
  34. Dragsted LO, Gao Q, Scalbert A, Vergeres G, Kolehmainen M et al. 2018. Validation of biomarkers of food intake-critical assessment of candidate biomarkers. Genes Nutr 13:14
    [Google Scholar]
  35. Drenos F 2017. Mechanistic insights from combining genomics with metabolomics. Curr. Opin. Lipidol. 28:99–103
    [Google Scholar]
  36. Edmands WM, Beckonert OP, Stella C, Campbell A, Lake BG et al. 2011. Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. J. Proteome Res. 10:4513–21
    [Google Scholar]
  37. Edmands WM, Ferrari P, Rothwell JA, Rinaldi S, Slimani N et al. 2015. Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries. Am. J. Clin. Nutr. 102:905–13
    [Google Scholar]
  38. Emwas AH 2015. The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol. Biol. 1277:161–93
    [Google Scholar]
  39. Emwas AH, Saccenti E, Gao X, McKay RT, dos Santos V et al. 2018. Recommended strategies for spectral processing and post-processing of 1D H-1-NMR data of biofluids with a particular focus on urine. Metabolomics 14:23
    [Google Scholar]
  40. Fan TWM, Lane AN 2016. Applications of NMR spectroscopy to systems biochemistry. Prog. Nucl. Magn. Reson. Spectrosc. 92–93:18–53
    [Google Scholar]
  41. Fiehn O 2016. Metabolomics by gas chromatography-mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114:30.4.1–30.4.32
    [Google Scholar]
  42. Frahnow T, Osterhoff MA, Hornemann S, Kruse M, Surma MA et al. 2017. Heritability and responses to high fat diet of plasma lipidomics in a twin study. Sci. Rep. 7:3750
    [Google Scholar]
  43. Gao Q, Pratico G, Scalbert A, Vergeres G, Kolehmainen M et al. 2017. A scheme for a flexible classification of dietary and health biomarkers. Genes Nutr 12:34
    [Google Scholar]
  44. Garcia-Aloy M, Llorach R, Urpi-Sarda M, Jáuregui O, Corella D et al. 2015.a A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study. Mol. Nutr. Food Res. 59:212–20
    [Google Scholar]
  45. Garcia-Aloy M, Llorach R, Urpi-Sarda M, Tulipani S, Estruch R et al. 2014. Novel multimetabolite prediction of walnut consumption by a urinary biomarker model in a free-living population: the PREDIMED study. J. Proteome Res. 13:3476–83
    [Google Scholar]
  46. Garcia-Aloy M, Llorach R, Urpi-Sarda M, Tulipani S, Salas-Salvado J et al. 2015.b Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort. Metabolomics 11:155–65
    [Google Scholar]
  47. García-Pérez I, Posma JM, Chambers ES, Nicholson JK, Mathers JC et al. 2016. An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake. J. Agric. Food Chem. 64:2423–31
    [Google Scholar]
  48. García-Pérez I, Posma JM, Gibson R, Chambers ES, Hansen TH et al. 2017. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endocrinol 5:184–95
    [Google Scholar]
  49. German JB, Gillies LA, Smilowitz JT, Zivkovic AM, Watkins SM 2007. Lipidomics and lipid profiling in metabolomics. Curr. Opin. Lipidol. 18:66–71
    [Google Scholar]
  50. Gibbons H, Carr E, McNulty BA, Nugent AP, Walton J et al. 2017.a Metabolomic-based identification of clusters that reflect dietary patterns. Mol. Nutr. Food Res 61: https://doi.org/10.1002/mnfr.201601050
    [Crossref] [Google Scholar]
  51. Gibbons H, McNulty BA, Nugent AP, Walton J, Flynn A et al. 2015.a A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake. Am. J. Clin. Nutr. 101:471–77
    [Google Scholar]
  52. Gibbons H, Michielsen CJR, Rundle M, Frost G, McNulty BA et al. 2017.b Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example. Mol. Nutr. Food Res. 61: https://doi.org/10.1002/mnfr.201700037
    [Crossref] [Google Scholar]
  53. Gibbons H, O'Gorman A, Brennan L 2015.b Metabolomics as a tool in nutritional research. Curr. Opin. Lipidol. 26:30–34
    [Google Scholar]
  54. González-Domínguez R, Sayago A, Fernández-Recamales Á 2017. Direct infusion mass spectrometry for metabolomic phenotyping of diseases. Bioanalysis 9:131–48
    [Google Scholar]
  55. Gorrochategui E, Jaumot J, Lacorte S, Tauler R 2016. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. Trends Anal. Chem. 82:425–42
    [Google Scholar]
  56. Gowda GAN, Raftery D 2017. Recent advances in NMR-based metabolomics. Anal. Chem. 89:490–510
    [Google Scholar]
  57. Guertin KA, Moore SC, Sampson JN, Huang WY, Xiao Q et al. 2014. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am. J. Clin. Nutr. 100:208–17
    [Google Scholar]
  58. Guo B, Chen B, Liu AM, Zhu WT, Yao SZ 2012. Liquid chromatography-mass spectrometric multiple reaction monitoring-based strategies for expanding targeted profiling towards quantitative metabolomics. Curr. Drug Metab. 13:1226–43
    [Google Scholar]
  59. Hanhineva K, Brunius C, Andersson A, Marklund M, Juvonen R et al. 2015.a Discovery of urinary biomarkers of whole grain rye intake in free-living subjects using nontargeted LC-MS metabolite profiling. Mol. Nutr. Food Res. 59:2315–25
    [Google Scholar]
  60. Hanhineva K, Keski-Rahkonen P, Lappi J, Katina K, Pekkinen J et al. 2014. The postprandial plasma rye fingerprint includes benzoxazinoid-derived phenylacetamide sulfates. J. Nutr. 144:1016–22
    [Google Scholar]
  61. Hanhineva K, Lankinen MA, Pedret A, Schwab U, Kolehmainen M et al. 2015.b Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J. Nutr. 145:7–17
    [Google Scholar]
  62. Heilbronn LK, Coster AC, Campbell LV, Greenfield JR, Lange K et al. 2013. The effect of short-term overfeeding on serum lipids in healthy humans. Obesity 21:E649–59
    [Google Scholar]
  63. Heinzmann SS, Brown IJ, Chan Q, Bictash M, Dumas ME et al. 2010. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am. J. Clin. Nutr. 92:436–43
    [Google Scholar]
  64. Heinzmann SS, Holmes E, Kochhar S, Nicholson JK, Schmitt-Kopplin P 2015. 2-Furoylglycine as a candidate biomarker of coffee consumption. J. Agric. Food Chem. 63:8615–21
    [Google Scholar]
  65. Hjerpsted JB, Ritz C, Schou SS, Tholstrup T, Dragsted LO 2014. Effect of cheese and butter intake on metabolites in urine using an untargeted metabolomics approach. Metabolomics 10:1176–85
    [Google Scholar]
  66. Ibero-Baraibar I, Romo-Hualde A, González-Navarro CJ, Zulet MA, Martinez JA 2016. The urinary metabolomic profile following the intake of meals supplemented with a cocoa extract in middle-aged obese subjects. Food Funct 7:1924–31
    [Google Scholar]
  67. Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA 2009. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum. Genet. 125:507–25
    [Google Scholar]
  68. Johansson-Persson A, Barri T, Ulmius M, Onning G, Dragsted LO 2013. LC-QTOF/MS metabolomic profiles in human plasma after a 5-week high dietary fiber intake. Anal. Bioanal. Chem. 405:4799–809
    [Google Scholar]
  69. Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G et al. 2012. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28:373–80
    [Google Scholar]
  70. Khakimov B, Poulsen SK, Savorani F, Acar E, Gurdeniz G et al. 2016. New Nordic diet versus average Danish diet: a randomized controlled trial revealed healthy long-term effects of the new Nordic diet by GC-MS blood plasma metabolomics. J. Proteome Res. 15:1939–54
    [Google Scholar]
  71. Khamis MM, Adamko DJ, El-Aneed A 2017. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom. Rev. 36:115–34
    [Google Scholar]
  72. Khymenets O, Andres-Lacueva C, Urpi-Sarda M, Vazquez-Fresno R, Mart MM et al. 2015. Metabolic fingerprint after acute and under sustained consumption of a functional beverage based on grape skin extract in healthy human subjects. Food Funct 6:1288–98
    [Google Scholar]
  73. Kien CL, Bunn JY, Stevens R, Bain J, Ikayeva O et al. 2014. Dietary intake of palmitate and oleate has broad impact on systemic and tissue lipid profiles in humans. Am. J. Clin. Nutr. 99:436–45
    [Google Scholar]
  74. Kim YJ, Huh I, Kim JY, Park S, Ryu SH et al. 2017. Integration of traditional and metabolomics biomarkers identifies prognostic metabolites for predicting responsiveness to nutritional intervention against oxidative stress and inflammation. Nutrients 9:233 https://doi.org/10.3390/nu9030233
    [Crossref] [Google Scholar]
  75. Kipnis V, Midthune D, Freedman L, Bingham S, Day NE et al. 2002. Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr 5:915–23
    [Google Scholar]
  76. Kobayashi M, Hanaoka T, Hashimoto H, Tsugane S 2005. 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) level in human hair as biomarkers for dietary grilled/stir-fried meat and fish intake. Mutat. Res. 588:136–42
    [Google Scholar]
  77. Kulp KS, Knize MG, Fowler ND, Salmon CP, Felton JS 2004. PhIP metabolites in human urine after consumption of well-cooked chicken. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 802:143–53
    [Google Scholar]
  78. Lacroix S, Lauria M, Scott-Boyer MP, Marchetti L, Priami C, Caberlotto L 2015. Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes. Genes Nutr 10:58
    [Google Scholar]
  79. Lahti L, Salonen A, Kekkonen RA, Salojarvi J, Jalanka-Tuovinen J et al. 2013. Associations between the human intestinal microbiota, Lactobacillus rhamnosus GG and serum lipids indicated by integrated analysis of high-throughput profiling data. PeerJ 1:e32
    [Google Scholar]
  80. Lampe JW, Navarro SL, Hullar MA, Shojaie A 2013. Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations. Proc. Nutr. Soc. 72:207–18
    [Google Scholar]
  81. Lankinen M, Schwab U, Erkkila A, Seppanen-Laakso T, Hannila ML et al. 2009. Fatty fish intake decreases lipids related to inflammation and insulin signaling: a lipidomics approach. PLOS ONE 4:e5258
    [Google Scholar]
  82. Lankinen M, Schwab U, Seppanen-Laakso T, Mattila I, Juntunen K et al. 2011. Metabolomic analysis of plasma metabolites that may mediate effects of rye bread on satiety and weight maintenance in postmenopausal women. J. Nutr. 141:31–36
    [Google Scholar]
  83. Larmo PS, Kangas AJ, Soininen P, Lehtonen HM, Suomela JP et al. 2013. Effects of sea buckthorn and bilberry on serum metabolites differ according to baseline metabolic profiles in overweight women: a randomized crossover trial. Am. J. Clin. Nutr. 98:941–51
    [Google Scholar]
  84. Lenz EM, Bright J, Wilson ID, Hughes A, Morrisson J et al. 2004. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. J. Pharm. Biomed. Anal. 36:841–49
    [Google Scholar]
  85. Llorach R, Garrido I, Monagas M, Urpi-Sarda M, Tulipani S et al. 2010. Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) DA Webb) skin polyphenols. J. Proteome Res. 9:5859–67
    [Google Scholar]
  86. Llorach R, Medina S, García-Viguera C, Zafrilla P, Abellán J et al. 2014. Discovery of human urinary biomarkers of aronia-citrus juice intake by HPLC-q-TOF-based metabolomic approach. Electrophoresis 35:1599–606
    [Google Scholar]
  87. Llorach R, Urpi-Sarda M, Jauregui O, Monagas M, Andres-Lacueva C 2009. An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. J. Proteome Res. 8:5060–68
    [Google Scholar]
  88. Llorach-Asunción R, Jauregui O, Urpi-Sarda M, Andres-Lacueva C 2010. Methodological aspects for metabolome visualization and characterization: a metabolomic evaluation of the 24 h evolution of human urine after cocoa powder consumption. J. Pharm. Biomed. Anal. 51:373–81
    [Google Scholar]
  89. Lloyd AJ, Beckmann M, Fave G, Mathers JC, Draper J 2011.a Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. Br. J. Nutr. 106:812–24
    [Google Scholar]
  90. Lloyd AJ, Fave G, Beckmann M, Lin W, Tailliart K et al. 2011.b Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. Am. J. Clin. Nutr. 94:981–91
    [Google Scholar]
  91. Madrid-Gambin F, Llorach R, Vázquez-Fresno R, Urpi-Sarda M, Almanza-Aguilera E et al. 2017. Urinary 1H nuclear magnetic resonance metabolomic fingerprinting reveals biomarkers of pulse consumption related to energy-metabolism modulation in a subcohort from the PREDIMED study. J. Proteome Res. 16:1483–91
    [Google Scholar]
  92. Marco-Ramell A, Palau-Rodriguez M, Alay A, Tulipani S, Urpi-Sardà M et al. 2018. Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinform 19:1
    [Google Scholar]
  93. Markley JL, Bruschweiler R, Edison AS, Eghbalnia HR, Powers R et al. 2017. The future of NMR-based metabolomics. Curr. Opin. Biotechnol. 43:34–40
    [Google Scholar]
  94. Martin FP, Moco S, Montoliu I, Collino S, Da Silva L et al. 2014. Impact of breast-feeding and high- and low-protein formula on the metabolism and growth of infants from overweight and obese mothers. Pediatr. Res. 75:535–43
    [Google Scholar]
  95. Martin FP, Montoliu I, Nagy K, Moco S, Collino S et al. 2012. Specific dietary preferences are linked to differing gut microbial metabolic activity in response to dark chocolate intake. J. Proteome Res. 11:6252–63
    [Google Scholar]
  96. May DH, Navarro SL, Ruczinski I, Hogan J, Ogata Y et al. 2013. Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study. Br. J. Nutr. 110:1760–70
    [Google Scholar]
  97. Meikle PJ, Barlow CK, Mellett NA, Mundra PA, Bonham MP et al. 2015. Postprandial plasma phospholipids in men are influenced by the source of dietary fat. J. Nutr. 145:2012–18
    [Google Scholar]
  98. Mennen LI, Sapinho D, Ito H, Bertrais S, Galan P et al. 2006. Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. Br. J. Nutr. 96:191–98
    [Google Scholar]
  99. Miccheli A, Marini F, Capuani G, Miccheli AT, Delfini M et al. 2009. The influence of a sports drink on the postexercise metabolism of elite athletes as investigated by NMR-based metabolomics. J. Am. Coll. Nutr. 28:553–64
    [Google Scholar]
  100. Misra BB, Fahrmann JF, Grapov D 2017. Review of emerging metabolomic tools and resources: 2015–2016. Electrophoresis 38:2257–74
    [Google Scholar]
  101. Moazzami AA, Shrestha A, Morrison DA, Poutanen K, Mykkanen H 2014. Metabolomics reveals differences in postprandial responses to breads and fasting metabolic characteristics associated with postprandial insulin demand in postmenopausal women. J. Nutr. 144:807–14
    [Google Scholar]
  102. Moazzami AA, Zhang JX, Kamal-Eldin A, Aman P, Hallmans G et al. 2011. Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. J. Nutr. 141:2126–32
    [Google Scholar]
  103. Moreira V, Brasili E, Fiamoncini J, Marini F, Miccheli A et al. 2018. Orange juice affects acylcarnitine metabolism in healthy volunteers as revealed by a mass-spectrometry based metabolomics approach. Food Res. Int. 107:346–52
    [Google Scholar]
  104. Morris C, O'Grada C, Ryan M, Roche HM, Gibney MJ et al. 2013. Identification of differential responses to an oral glucose tolerance test in healthy adults. PLOS ONE 8:e72890
    [Google Scholar]
  105. Mulder TP, Rietveld AG, van Amelsvoort JM 2005. Consumption of both black tea and green tea results in an increase in the excretion of hippuric acid into urine. Am. J. Clin. Nutr. 81:256s–60s
    [Google Scholar]
  106. Munger LH, Trimigno A, Picone G, Freiburghaus C, Pimentel G et al. 2017. Identification of urinary food intake biomarkers for milk, cheese, and soy-based drink by untargeted GC-MS and NMR in healthy humans. J. Proteome Res. 16:3321–35
    [Google Scholar]
  107. Myint T, Fraser GE, Lindsted KD, Knutsen SF, Hubbard RW, Bennett HW 2000. Urinary 1-methylhistidine is a marker of meat consumption in Black and in White California Seventh-day Adventists. Am. J. Epidemiol. 152:752–55
    [Google Scholar]
  108. Nagy K, Redeuil K, Williamson G, Rezzi S, Dionisi F et al. 2011. First identification of dimethoxycinnamic acids in human plasma after coffee intake by liquid chromatography-mass spectrometry. J. Chromatogr. A 1218:491–97
    [Google Scholar]
  109. Nestel PJ, Mellett N, Pally S, Wong G, Barlow CK et al. 2013. Effects of low-fat or full-fat fermented and non-fermented dairy foods on selected cardiovascular biomarkers in overweight adults. Br. J. Nutr. 110:2242–49
    [Google Scholar]
  110. Ni Y, Jensen K, Kouskoumvekaki I, Panagiotou G 2017. NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy. Database 2017:bax044
    [Google Scholar]
  111. Nicholson JK 2006. Global systems biology, personalized medicine and molecular epidemiology. Mol. Syst. Biol. 2:6
    [Google Scholar]
  112. O'Donovan CB, Walsh MC, Nugent AP, McNulty B, Walton J et al. 2015. Use of metabotyping for the delivery of personalised nutrition. Mol. Nutr. Food Res. 59:377–85
    [Google Scholar]
  113. O'Donovan CB, Walsh MC, Woolhead C, Forster H, Celis-Morales C et al. 2017. Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study. Br. J. Nutr. 118:561–69
    [Google Scholar]
  114. O'Sullivan A, Gibney MJ, Brennan L 2011.a Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am. J. Clin. Nutr. 93:314–21
    [Google Scholar]
  115. O'Sullivan A, Gibney MJ, Connor AO, Mion B, Kaluskar S et al. 2011.b Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol. Nutr. Food Res. 55:679–90
    [Google Scholar]
  116. Paley S, O'Maille PE, Weaver D, Karp PD 2016. Pathway collages: personalized multi-pathway diagrams. BMC Bioinform 17:529
    [Google Scholar]
  117. Pallister T, Haller T, Thorand B, Altmaier E, Cassidy A et al. 2017. Metabolites of milk intake: a metabolomic approach in UK twins with findings replicated in two European cohorts. Eur. J. Nutr. 56:2379–91
    [Google Scholar]
  118. Panagiotou G, Nielsen J 2009. Nutritional systems biology: definitions and approaches. Annu. Rev. Nutr. 29:329–39
    [Google Scholar]
  119. Park YJ, Volpe SL, Decker EA 2005. Quantitation of carnosine in humans plasma after dietary consumption of beef. J. Agric. Food Chem. 53:4736–39
    [Google Scholar]
  120. Piccolo BD, Comerford KB, Karakas SE, Knotts TA, Fiehn O, Adams SH 2015. Whey protein supplementation does not alter plasma branched-chained amino acid profiles but results in unique metabolomics patterns in obese women enrolled in an 8-week weight loss trial. J. Nutr. 145:691–700
    [Google Scholar]
  121. Pistollato F, Calderón Iglesias R, Ruiz R, Aparicio S, Crespo J et al. 2018. Nutritional patterns associated with the maintenance of neurocognitive functions and the risk of dementia and Alzheimer's disease: a focus on human studies. Pharmacol. Res. 131:32–43
    [Google Scholar]
  122. Pontes JGM, Brasil AJM, Cruz GCF, de Souza RN, Tasic L 2017. NMR-based metabolomics strategies: plants, animals and humans. Anal. Methods 9:1078–96
    [Google Scholar]
  123. Posma JM, Garcia-Perez I, Heaton JC, Burdisso P, Mathers JC et al. 2017. Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers. Anal. Chem. 89:3300–9
    [Google Scholar]
  124. Prentice RL, Mossavar-Rahmani Y, Huang Y, Van Horn L, Beresford SA et al. 2011. Evaluation and comparison of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers. Am. J. Epidemiol. 174:591–603
    [Google Scholar]
  125. Pujos-Guillot E, Hubert J, Martin J-F, Lyan B, Quintana M et al. 2013. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. J. Proteome Res. 12:1645–59
    [Google Scholar]
  126. Rådjursöga M, Karlsson GB, Lindqvist HM, Pedersen A, Persson C et al. 2017. Metabolic profiles from two different breakfast meals characterized by 1H NMR-based metabolomics. Food Chem 231:267–74
    [Google Scholar]
  127. Rangel-Huerta OD, Aguilera CM, Perez-de-la-Cruz A, Vallejo F, Tomas-Barberan F et al. 2017. A serum metabolomics-driven approach predicts orange juice consumption and its impact on oxidative stress and inflammation in subjects from the BIONAOS study. Mol. Nutr. Food Res 61: https://doi.org/10.1002/mnfr.201600120
    [Crossref] [Google Scholar]
  128. Rangel-Huerta OD, Gil A 2016. Nutrimetabolomics: an update on analytical approaches to investigate the role of plant-based foods and their bioactive compounds in non-communicable chronic diseases. Int. J. Mol. Sci. 17:16
    [Google Scholar]
  129. Rasmussen LG, Winning H, Savorani F, Ritz C, Engelsen SB et al. 2012. Assessment of dietary exposure related to dietary GI and fibre intake in a nutritional metabolomic study of human urine. Genes Nutr 7:281–93
    [Google Scholar]
  130. Reistad R, Rossland OJ, Latva-Kala KJ, Rasmussen T, Vikse R et al. 1997. Heterocyclic aromatic amines in human urine following a fried meat meal. Food Chem. Toxicol. 35:945–55
    [Google Scholar]
  131. Ross AB, Svelander C, Undeland I, Pinto R, Sandberg AS 2015. Herring and beef meals lead to differences in plasma 2-aminoadipic acid, β-alanine, 4-hydroxyproline, cetoleic acid, and docosahexaenoic acid concentrations in overweight men. J. Nutr. 145:2456–63
    [Google Scholar]
  132. Rothwell JA, Fillatre Y, Martin JF, Lyan B, Pujos-Guillot E et al. 2014. New biomarkers of coffee consumption identified by the non-targeted metabolomic profiling of cohort study subjects. PLOS ONE 9:e93474
    [Google Scholar]
  133. Rudkowska I, Paradis AM, Thifault E, Julien P, Tchernof A et al. 2013. Transcriptomic and metabolomic signatures of an n-3 polyunsaturated fatty acids supplementation in a normolipidemic/normocholesterolemic Caucasian population. J. Nutr. Biochem. 24:54–61
    [Google Scholar]
  134. Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS et al. 2009. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5:435–58
    [Google Scholar]
  135. Schmedes M, Balderas C, Aadland ED, Jacques H, Lavigne C et al. 2018. The effect of lean-seafood and non-seafood diets on fasting and postprandial serum metabolites and lipid species: results from a randomized crossover intervention study in healthy adults. Nutrients 10:598
    [Google Scholar]
  136. Schmedes MS, Yde CC, Svensson U, Håkansson J, Baby S, Bertram HC 2015. Impact of a 6-week very low-calorie diet and weight reduction on the serum and fecal metabolome of overweight subjects. Eur. Food Res. Technol. 240:583–94
    [Google Scholar]
  137. Shrestha A, Mullner E, Poutanen K, Mykkanen H, Moazzami AA 2017. Metabolic changes in serum metabolome in response to a meal. Eur. J. Nutr. 56:671–81
    [Google Scholar]
  138. Sperisen P, Cominetti O, Martin FPJ 2015. Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research. Front. Mol. Biosci. 2:44
    [Google Scholar]
  139. Stalmach A, Mullen W, Barron D, Uchida K, Yokota T et al. 2009. Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption. Drug Metab. Dispos. 37:1749–58
    [Google Scholar]
  140. Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC et al. 2006. Susceptibility of human metabolic phenotypes to dietary modulation. J. Proteome Res. 5:2780–88
    [Google Scholar]
  141. Strickland PT, Qian Z, Friesen MD, Rothman N, Sinha R 2002. Metabolites of 2-amino-1-methyl-6-phenylimidazo(4,5-b)pyridine (PhIP) in human urine after consumption of charbroiled or fried beef. Mutat. Res. 506–507:163–73
    [Google Scholar]
  142. Tsugawa H 2018. Advances in computational metabolomics and databases deepen the understanding of metabolisms. Curr. Opin. Biotechnol. 54:10–17
    [Google Scholar]
  143. Urpi-Sarda M, Boto-Ordóñez M, Queipo-Ortuno MI, Tulipani S, Corella D et al. 2015. Phenolic and microbial-targeted metabolomics to discovering and evaluating wine intake biomarkers in human urine and plasma. Electrophoresis 36:2259–68
    [Google Scholar]
  144. Vandeputte D, Falony G, Vieira-Silva S, Wang J, Sailer M et al. 2017. Prebiotic inulin-type fructans induce specific changes in the human gut microbiota. Gut 66:1968–74
    [Google Scholar]
  145. van der Hooft JJ, de Vos RC, Mihaleva V, Bino RJ, Ridder L et al. 2012. Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake. Anal. Chem. 84:7263–71
    [Google Scholar]
  146. Van Dorsten FA, Daykin CA, Mulder TP, Van Duynhoven JP 2006. Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J. Agric. Food Chem. 54:6929–38
    [Google Scholar]
  147. van Duynhoven JPM, Jacobs DM 2016. Assessment of dietary exposure and effect in humans: the role of NMR. Prog. Nucl. Magn. Reson. Spectrosc. 96:58–72
    [Google Scholar]
  148. van Ommen B, van den Broek T, de Hoogh I, van Erk M, van Someren E et al. 2017. Systems biology of personalized nutrition. Nutr. Rev. 75:579–99
    [Google Scholar]
  149. van Velzen EJ, Westerhuis JA, van Duynhoven JP, van Dorsten FA, Grun CH et al. 2009. Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J. Proteome Res 8:3317–30
    [Google Scholar]
  150. Vázquez-Fresno R, Llorach R, Alcaro F, Rodríguez MA, Vinaixa M et al. 2012. 1H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors. Electrophoresis 33:2345–54
    [Google Scholar]
  151. Vázquez-Fresno R, Llorach R, Perera A, Mandal R, Feliz M et al. 2016. Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake. J. Nutr. Biochem. 28:114–20
    [Google Scholar]
  152. Vázquez-Fresno R, Llorach R, Urpi-Sarda M, Khymenets O, Bullo M et al. 2015. An NMR metabolomics approach reveals a combined-biomarkers model in a wine interventional trial with validation in free-living individuals of the PREDIMED study. Metabolomics 11:797–806
    [Google Scholar]
  153. Walsh MC, Brennan L, Pujos-Guillot E, Sebedio JL, Scalbert A et al. 2007. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am. J. Clin. Nutr. 86:1687–93
    [Google Scholar]
  154. Wang TT, Edwards AJ, Clevidence BA 2013. Strong and weak plasma response to dietary carotenoids identified by cluster analysis and linked to beta-carotene 15,15′-monooxygenase 1 single nucleotide polymorphisms. J. Nutr. Biochem. 24:1538–46
    [Google Scholar]
  155. Wong M, Lodge JK 2012. A metabolomic investigation of the effects of vitamin E supplementation in humans. Nutr. Metab. 9:110
    [Google Scholar]
  156. Xu J, Yang S, Cai S, Dong J, Li X, Chen Z 2010. Identification of biochemical changes in lactovegetarian urine using 1H NMR spectroscopy and pattern recognition. Anal. Bioanal. Chem. 396:1451–63
    [Google Scholar]
  157. Yin X, Gibbons H, Rundle M, Frost G, McNulty BA et al. 2017. Estimation of chicken intake by adults using metabolomics-derived markers. J. Nutr. 147:1850–57
    [Google Scholar]
  158. Yu XT, Zeng T 2018. Integrative analysis of omics big data. Meth. Mol. Biol. 1754:109–35
    [Google Scholar]
  159. Zak A, Burda M, Vecka M, Zeman M, Tvrzicka E, Stankova B 2014. Fatty acid composition indicates two types of metabolic syndrome independent of clinical and laboratory parameters. Physiol. Res. 63:S375–S85
    [Google Scholar]
/content/journals/10.1146/annurev-food-032818-121715
Loading
/content/journals/10.1146/annurev-food-032818-121715
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error